MetaMed: Few-shot medical image classification using gradient-based meta-learning

• Published in: Pattern recognition Vol. 120; p. 108111
• Main Authors: Singh, Rishav, Bharti, Vandana, Purohit, Vishal, Kumar, Abhinav, Singh, Amit Kumar, Singh, Sanjay Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2021
• Subjects: Few-shot learning; Histopathological image classification; Image augmentation; Medical image classification; Meta-learning; Multi-shot learning; Histopathological image classification; Image augmentation; Multi-shot learning; Few-shot learning; Meta-learning; Medical image classification
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2021.108111

•Efficacy of a gradient-based meta-learning algorithm for few-shot learning problem on real-world non-uniformly distributed medical image datasets is analyzed.•Our experiments empirically validate that the use of meta-learning increases the confidence of predictions and robustness.•Our work proves that normal augmentation strategies fail to regularize the network in gradient-based meta-learning problems.•Hence, we integrated advanced augmentation strategies that can generate virtual samples as well as labels.•Showcasing the advantages of advanced augmentation techniques on three complex medical image datasets.•Our work significantly reduces the need to collect and annotate large data for deep learning applications in the medical domain. The occurrence of long-tailed distributions and unavailability of high-quality annotated images is a common phenomenon in medical datasets. The use of conventional Deep Learning techniques to obtain an unbiased model with high generalization accuracy for such datasets is a challenging task. Thus, we formulated a few-shot learning problem and presented a meta-learning-based “MetaMed” approach. The model presented here can adapt to rare disease classes with the availability of few images, and less compute. MetaMed is validated on three publicly accessible medical datasets – Pap smear, BreakHis, and ISIC 2018. We used advanced image augmentation techniques like CutOut, MixUp, and CutMix to overcome the problem of over-fitting. Our approach has shown promising results on all the three datasets with an accuracy of more than 70%. Inclusion of advanced augmentation techniques regularizes the model and increases the generalization capability by  2–5%. Comparative analysis of MetaMed against transfer learning demonstrated that MetaMed classifies images with a higher confidence score and on average outperforms transfer learning for 3, 5, and 10-shot tasks for both 2-way and 3-way classification.